Rural sewage treatment result prediction method and system

Abstract

The invention relates to the technical field of sewage treatment and intelligent detection, in particular to a rural sewage treatment result prediction method and system, and the method comprises the steps: collecting a rural sewage treatment data set through a rural sewage treatment station; establishing a sewage treatment data processing mechanism, processing the rural sewage treatment data set by using the sewage treatment data processing mechanism, and obtaining a target prediction data set; constructing a rural sewage treatment result prediction analysis system, and combining the target prediction data set and the prediction analysis system to obtain an evaluation prediction result of the prediction analysis system; and establishing a sewage treatment index correlation analysis model, and performing comprehensive evaluation analysis on the rural sewage treatment result in combination with the sewage treatment index correlation analysis model, the evaluation prediction result and the target prediction data set. The rural sewage treatment result is comprehensively evaluated and analyzed based on the data processing model, the prediction analysis system and the correlation analysis model, and the detection efficiency and the result accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment and intelligent detection, and in particular to a method and system for predicting rural sewage treatment results. Background Art

[0002] With the improvement of rural living standards and the increase in water consumption, the discharge of rural domestic sewage is also increasing. If the sewage is not properly treated, it will cause serious pollution to the rural environment and affect the quality of life and health of farmers. Therefore, the effective detection of rural sewage treatment results is an urgent problem to be solved.

[0003] Traditional prediction methods for rural sewage treatment results have obvious limitations. They often focus on monitoring a few water quality indicators and ignore the impact of other factors on sewage treatment results. At the same time, existing prediction methods mostly use simple statistical models or empirical formulas to process and analyze data, which makes it difficult to handle the complexity, real-time and nonlinear characteristics of the sewage treatment process, resulting in insufficient accuracy and credibility of the prediction results. On the other hand, the existing prediction methods lack a unified prediction evaluation system and correlation analysis mechanism, and are unable to fully integrate and consider the impact of multiple factors on sewage treatment results, and thus cannot ensure the comprehensiveness and accuracy of the prediction results.

[0004] In order to overcome the above challenges and problems, it is urgently necessary to introduce intelligent detection models and evaluation systems to provide technical support for the field of rural sewage treatment detection, and provide a more scientific and reliable decision-making basis for rural sewage treatment, which is conducive to the continuous improvement of the rural ecological environment and the effective improvement of the quality of life. Summary of the invention

[0005] In view of the shortcomings of existing methods and the needs of practical applications, in order to scientifically and accurately evaluate the results of rural sewage treatment, it is necessary to construct corresponding data processing models, prediction analysis frameworks and correlation analysis models. The relevant models work together to predict and analyze the results of rural sewage treatment, which helps to understand the sewage treatment process more comprehensively, so as to achieve accurate detection of sewage treatment results, and thus formulate more scientific and reasonable sewage treatment strategies. On the one hand, the present invention provides a method for predicting rural sewage treatment results, which includes: collecting a rural sewage treatment data set through a rural sewage treatment station; establishing a sewage treatment data processing mechanism, using the sewage treatment data processing mechanism to process the rural sewage treatment data set, and obtaining a target prediction data set of the rural sewage treatment station; constructing a rural sewage treatment result prediction analysis system, combining the target prediction data set and the rural sewage treatment result prediction analysis system to obtain the evaluation prediction results of the prediction analysis system; establishing a sewage treatment index correlation analysis model, combining the sewage treatment index correlation analysis model, the evaluation prediction results and the target prediction data set to conduct a comprehensive evaluation and analysis of the rural sewage treatment results. The present invention conducts a comprehensive evaluation and analysis of the rural sewage treatment results. The data processing mechanism can improve the accuracy of system data, and the correlation analysis helps to improve the scientificity and effectiveness of decision-making, thereby providing strong technical support and decision-making basis for rural sewage treatment work, and promoting the continuous improvement and sustainable development of the rural ecological environment.

[0006] Optionally, the rural sewage treatment data set collected through the rural sewage treatment station includes: obtaining rural sewage treatment historical information based on the rural sewage treatment station; collecting rural sewage treatment real-time detection data through the rural sewage treatment station; and obtaining the rural sewage treatment data set by combining the rural sewage treatment historical information and the rural sewage treatment real-time detection data. The present invention collects historical information and real-time detection data through the rural sewage treatment station, which can improve the comprehensiveness and effectiveness of the data set.

[0007] Optionally, the establishment of the sewage treatment data processing mechanism includes: setting a data encoding model, a data decoding model and a data error analysis model based on the data time series characteristics; and establishing the sewage treatment data processing mechanism in combination with the data encoding model, the data decoding model and the data error analysis model. The sewage treatment data processing mechanism established by the present invention makes the data processing process more standardized and normalized.

[0008] Optionally, the data encoding model satisfies the following relationship:

[0009] in, Represents the encoding results of different time series data sets, Represents the nonlinear change function of the encoding process, represents the encoding model weight, express A collection of time series data, Indicates encoding bias; The data decoding model satisfies the following relationship:

[0010] in, Represents the decoding results of different time series data sets, Represents the constant corresponding to the decoding model, Represents the nonlinear variation function of the decoding process, represents the decoding model weight, Represents the encoding results of different time series data sets, represents the decoding bias; The data error analysis model satisfies the following relationship: , in, represents the error coefficient of different time series data sets, express The amount of data, The number of time series representing sewage treatment information, express The feature information matrix, express The corresponding coefficient of variation is express A collection of time series data, Represents the decoding results of different time series data sets.

[0011] The data encoding model, data decoding model and data error analysis model of the present invention can significantly improve data processing capabilities, optimize processing efficiency, and promote innovation and improvement of data processing technology.

[0012] Optionally, the method of using the sewage treatment data processing mechanism to process the rural sewage treatment data set and obtain the target prediction data set of the rural sewage treatment station includes: processing the rural sewage treatment data set through the data encoding model to obtain the encoding results of different time series data sets; using the data decoding model to decode the encoding results of different time series data sets to obtain the decoding results of different time series data sets; performing error analysis on the data sets and decoding results of different time series data sets according to the data error analysis model to obtain the abnormality determination results of different time series data sets; optimizing the different time series data sets based on the abnormality determination results to obtain the target prediction data set of the rural sewage treatment station. The data encoding and decoding process of the present invention adopts different algorithm models, which can quickly process a large amount of data, help shorten the data processing time, and improve work efficiency. At the same time, by analyzing the target prediction data set, bottlenecks and problems in the sewage treatment process can be found, which helps to formulate targeted sewage treatment strategies.

[0013] Optionally, the construction of the rural sewage treatment result prediction and analysis system includes: setting a reference index layer of the rural sewage treatment result prediction and analysis system based on the rural sewage treatment historical information, the reference index layer including a water quality reference index layer, a treatment efficiency reference index layer and a sewage process performance reference index layer. The present invention sets multiple reference index layers to analyze the rural sewage treatment results from different dimensions, thereby comprehensively reflecting the actual situation of rural sewage treatment.

[0014] Optionally, the evaluation prediction results of the prediction analysis system obtained by combining the target prediction data set and the rural sewage treatment result prediction analysis system include: obtaining BOD analysis results, COD analysis results, suspended solids analysis results and total phosphorus analysis results based on the water quality reference index layer; obtaining pollutant removal rate analysis results and sewage treatment volume analysis results according to the treatment efficiency reference index layer; obtaining sludge analysis results and sludge moisture content analysis results according to the sewage process performance reference index layer. The prediction analysis system of the present invention can provide real-time and accurate sewage treatment data and information for environmental regulatory departments, which is helpful for real-time monitoring and analysis of changes in water quality indicators.

[0015] Optionally, the BOD analysis result satisfies the following relationship:

[0016] in, Represents the biochemical oxygen demand in the sewage sample for 15 days, represents the initial dissolved oxygen of the sewage sample after dilution, It indicates the dissolved oxygen of the diluted sewage sample after being cultured in a constant temperature incubator for 15 days. Indicates the dilution ratio of the sewage sample; The COD analysis results satisfy the following relationship:

[0017] in, Represents the chemical oxygen demand in the sewage sample, The conversion factor representing the number of moles of oxygen required to oxidize 1 mol of organic matter and the number of moles of KMnO4, represents the molar mass of oxygen atoms, Indicates the difference in KMnO4 dosage before and after the sewage sample titration. Indicates the standard concentration of KMnO4, It indicates the volume of sewage sample after adding distilled water; The suspended matter analysis results satisfy the following relationship:

[0018] in, represents the calculation result of suspended solids in sewage samples, Indicates the mass of suspended matter in a sewage sample. Indicates the sewage sample flow rate; The total phosphorus analysis results satisfy the following relationship:

[0019] in, represents the total phosphorus concentration in the sewage sample, Represents the measured value of total phosphorus in sewage samples, Indicates the dilution factor of the sewage sample. Indicates the impact index for measuring total phosphorus content; The pollutant removal rate analysis results satisfy the following relationship:

[0020] in, It represents the removal rate of pollutant concentration in sewage samples, represents the initial pollutant concentration of the sewage sample, represents the initial volume of the sewage sample, represents the pollutant concentration of sewage after treatment on day n, Represents the volume of the sewage sample after treatment on day n The sludge analysis results satisfy the following relationship:

[0021] in, represents the sedimentation volume of sludge in the sewage sample, It represents the sedimentation efficiency of the sedimentation tank. represents the sewage sample flow rate, Indicates the suspended solids concentration of the sewage sample entering the sedimentation tank. Indicates the moisture content of sludge in sewage samples, represents the initial sludge concentration in the sedimentation tank, It represents the influencing parameter corresponding to the number of sewage treatment inlets in the sewage treatment system, Indicates the influencing parameter of the interval time of sludge discharge from the sedimentation tank in sewage treatment; The sludge moisture content analysis results satisfy the following relationship:

[0022] in, Indicates the moisture content of sludge in sewage samples, represents the total mass of sludge in the sewage sample, It indicates the mass of solid matter in sludge after removing water.

[0023] Different reference indicators of the present invention have different calculation formulas, which can significantly improve the accuracy of rural sewage treatment prediction results and comprehensive evaluation results.

[0024] Optionally, the establishing of a sewage treatment index correlation analysis model, and the comprehensive evaluation and analysis of rural sewage treatment results in combination with the sewage treatment index correlation analysis model, the evaluation prediction results and the target prediction data set include: establishing a sewage treatment index correlation analysis model based on rural sewage treatment standards; using the sewage treatment index correlation analysis model to obtain correlation coefficients of different reference indicators in a rural sewage treatment result prediction and analysis system; and comprehensively evaluating and analyzing rural sewage treatment results in combination with the correlation coefficients, the evaluation prediction results and the target prediction data set; The sewage treatment index correlation analysis model satisfies the following relationship:

[0025] in, Represents the correlation coefficient of different reference indicators in the rural sewage treatment result prediction and analysis system, Represents the measured average value of different reference indicators, express The corresponding weight coefficient is, Indicates the reference values ​​of different reference indicators.

[0026] The correlation analysis model of the present invention provides more scientific prediction information, helps to more accurately judge the operating status and treatment effect of the sewage treatment system, and is conducive to formulating more reasonable and effective sewage treatment strategies.

[0027] In the second aspect, in order to efficiently execute the rural sewage treatment result prediction method provided by the present invention, the present invention also provides a rural sewage treatment result prediction system, the above system includes an input device, a processor, an output device and a memory, wherein the input device, the processor, the output device and the memory are interconnected, the memory includes the rural sewage treatment result prediction method as described in the first aspect of the present invention, the above memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions. The rural sewage treatment result prediction system provided by the present invention has a compact structure, strong applicability, and greatly improves the operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart of the method for predicting rural sewage treatment results of the present invention; Figure 2 It is a flow chart of the sewage treatment data processing mechanism in the rural sewage treatment result prediction method of the present invention; Figure 3 This is a structural diagram of the rural sewage treatment result prediction system of the present invention. DETAILED DESCRIPTION

[0029] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0030] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0031] See also Figure 1In order to more comprehensively understand the sewage treatment process and formulate a more scientific and reasonable sewage treatment strategy, on the one hand, the sewage treatment data is cleaned, integrated and processed to ensure the accuracy and consistency of the data. On the other hand, the prediction system and correlation model are introduced to more deeply understand the interaction between different influencing factors in the sewage treatment process, quantify the correlation between different indicators, and provide a scientific basis for the optimization of sewage treatment technology and the formulation of sewage treatment strategies. The present invention provides a method for predicting rural sewage treatment results, which includes the following steps: S1. Collect rural sewage treatment data sets through rural sewage treatment stations. The specific setting steps and implementation contents are as follows: In this embodiment, rural sewage treatment historical information and rural sewage treatment real-time detection data are obtained based on rural sewage treatment stations; and rural sewage treatment data set is obtained by combining the rural sewage treatment historical information and rural sewage treatment real-time detection data.

[0032] First, based on the key facilities and devices in rural sewage treatment plants, the historical information and real-time detection data of rural sewage treatment are systematically obtained. The above process not only focuses on the collection of historical data, but also emphasizes the monitoring and integration of real-time data to ensure the timeliness and accuracy of the rural sewage treatment data set.

[0033] The rural sewage treatment data set includes but is not limited to influent water quality parameters: detailed records of various influent water quality indicators, such as chemical oxygen demand (COD), biological oxygen demand (BOD), ammonia nitrogen content, total phosphorus concentration, etc. The above indicators can intuitively reflect the original pollution level of rural sewage.

[0034] Effluent water quality parameters: Similarly, the treated effluent water quality is monitored in all aspects, and the data and changes of key indicators such as COD, BOD, ammonia nitrogen, and total phosphorus are recorded, which will help in the subsequent evaluation of the rural sewage treatment effect.

[0035] Treatment process parameters: At the same time, it is necessary to collect various process parameters in the sewage treatment process, including but not limited to the aeration volume, which will affect the dissolved oxygen content in the sewage, the sludge age, which can reflect the biological activity of the sludge, the return ratio, which adjusts the circulation ratio of sewage and sludge, etc. The relevant parameters are conducive to the subsequent optimization of sewage treatment processes and sewage treatment strategies.

[0036] Operation time record: Accurately record the operation time of the sewage treatment plant, including but not limited to daily operation time, downtime for maintenance, etc., which will help analyze the relationship between treatment efficiency and energy consumption of rural sewage treatment plants.

[0037] In the embodiment, on the basis of collecting historical data, the sewage treatment data in the rural sewage treatment station will be detected in real time. Online monitoring equipment and sensor devices are installed in the rural sewage treatment station to collect various data in the sewage treatment process in real time, such as flow, pressure, temperature, etc., and upload them to the data processing center in real time, further realizing the dynamic update and real-time analysis of the detection data of the rural sewage treatment station.

[0038] In summary, by integrating historical information and real-time detection data, a comprehensive, accurate and timely rural sewage treatment data set was obtained, which provided strong support for subsequent sewage treatment data analysis, process optimization and environmental protection decision-making.

[0039] Furthermore, the method for obtaining the rural sewage treatment data set in this embodiment is only an optional condition of the present invention. In one or some other embodiments, the method for obtaining the rural sewage treatment data set can be updated according to the actual operation status of the rural sewage treatment station and the prediction requirements of the sewage treatment results, which can ensure that the collected data is more in line with the actual operation status of the rural sewage treatment station. Reduce unnecessary data collection work, improve the pertinence and effectiveness of data sets, and provide more valuable information for subsequent data analysis and effect evaluation.

[0040] S2. Establish a sewage treatment data processing mechanism, use the sewage treatment data processing mechanism to process the rural sewage treatment data set, and obtain the target prediction data set of rural sewage treatment stations. The specific steps and implementation contents are as follows: The rural sewage treatment result prediction method also includes preprocessing the rural sewage treatment data set in advance. In the process of obtaining the rural sewage treatment data set, a series of preprocessing operations are performed on the collected raw data, and the above preprocessing operations include but are not limited to data cleaning, denoising and normalization processing, based on which the quality of the data set and the accuracy of the calculation model can be guaranteed.

[0041] The specific contents of the above data preprocessing operations are as follows: Data cleaning focuses on identifying and correcting errors, missing values ​​or outliers in rural sewage treatment data sets. The integrity and consistency of rural sewage treatment data sets can be ensured by deleting invalid records, filling missing data and correcting obvious errors.

[0042] Data denoising needs to take into account the random fluctuations or measurement errors in the data monitoring process. The data denoising operation can smooth the relevant data and reduce noise interference. The embodiment uses filtering technology and statistical methods to extract the real signal while weakening or eliminating unnecessary noise information.

[0043] Since different water quality parameters and treatment process parameters have completely different numerical ranges and dimensions, the data set needs to be normalized. Converting all data in the rural sewage treatment data set to the same scale can not only simplify the model training process, but also improve the convergence speed and prediction performance of different models, further ensuring the comparability and consistency of different data features for model decisions.

[0044] In summary, pre-processing measures such as cleaning, denoising and normalization of the rural sewage treatment data set in advance can improve the data quality of the rural sewage treatment data set, enhance the generalization ability of the model, and ensure the accuracy of the prediction results, providing strong data support for the optimization of rural sewage treatment technology and environmental management decisions.

[0045] In order to obtain higher quality rural sewage treatment data. Firstly, the data encoding model, data decoding model and data error analysis model are set up based on the data time series characteristics; at the same time, the sewage treatment data processing mechanism is established by combining the above data encoding model, data decoding model and data error analysis model.

[0046] In this embodiment, the rural sewage treatment data follows a certain time pattern, and then a data set based on different time series is constructed based on the data collection rules and time interval rules. The above data set is composed of multiple time series data subsets, which can comprehensively record and analyze various parameters and operating status of sewage treatment.

[0047] In the embodiment, the rural sewage treatment data set needs to satisfy the following relationship:

[0048] in, represents the rural sewage treatment data set, Represents a data set of 1 time series, Represents a data set of 2 time series, express A collection of time series data. Each of the above They all contain monitoring information on sewage treatment parameters or operating status within a specific time period.

[0049] Based on the data collection rules, different time series data subsets are integrated into a unified data set for subsequent centralized data management and analysis, which helps to ensure the integrity and consistency of the data set and facilitates subsequent data processing and mining. On the other hand, the time series information in the rural sewage treatment data set can be more effectively utilized to better understand the actual situation of sewage treatment, discover potential problems and improvement points, and provide a scientific basis for optimizing sewage treatment plans.

[0050] In this embodiment, the automatic encoder anomaly detection mechanism is optimized and the sewage treatment data processing mechanism in the embodiment is established. The core of the mechanism is to capture the potential features in the original input data through the encoding process and reconstruct the relevant data using the decoding model.

[0051] A data encoding model is established to encode the above time series data set. The above data encoding model satisfies the following relationship:

[0052] in, Represents the encoding results of different time series data sets, Represents the nonlinear change function of the encoding process, represents the encoding model weight, express A collection of time series data, Indicates encoding bias; The encoding results of different time series data sets are encoding vectors output by the data encoding model, which capture the key information and features in different time series data sets.

[0053] The nonlinear change function of the encoding process can learn the nonlinear relationship in the data and map the input data to a higher-dimensional or lower-dimensional space to capture the complex characteristics of the data.

[0054] The encoding model weights are parameters learned by the model during the training process, which determine the importance of each feature in the input data and can linearly transform the input data into the latent feature space. The above weights can be optimized by the back-propagation algorithm to minimize the reconstruction error or other losses.

[0055] The data set of different time series is the input data of the nonlinear change function of the data encoding model process. The data set of different time series can be a multidimensional vector, in which each dimension represents a different feature or variable.

[0056] The encoding bias is a learnable parameter that allows the model to fine-tune the output of each neuron. During the encoding process, the bias term can adjust the baseline level of the encoding result, thereby increasing the flexibility of the model.

[0057] In summary, the above parameters together constitute a data encoding model, which maps the input data to the latent feature space through nonlinear transformation and linear combination, and generates corresponding encoding results. The above encoding process can capture the key information and features in the data, and provide information support for subsequent sewage data analysis, anomaly detection and result prediction.

[0058] Through the encoding process, the sewage treatment data processing mechanism can learn the key features in the data, and use the above features to reconstruct the original data in the decoding stage. If there is a significant difference between the reconstructed data and the original data, it indicates that there is an abnormality in the input data.

[0059] In order to reconstruct different time series data subsets, the data decoding model satisfies the following relationship:

[0060] in, Represents the decoding results of different time series data sets, Represents the constant corresponding to the decoding model, Represents the nonlinear variation function of the decoding process, represents the decoding model weight, Represents the encoding results of different time series data sets, represents the decoding bias; The decoding results of different time series data sets are the original data or approximate original data representations reconstructed by the data decoding model from the encoded data. The decoding process can restore the structure and information of the input data for comparison with the original data or for other data analysis tasks.

[0061] The constant corresponding to the decoding model is a learnable parameter or a fixed scaling factor, which can be used to adjust the amplitude or range of the decoded output to ensure that the decoded data is consistent in scale with the original data.

[0062] The nonlinear transformation function of the decoding process is similar to the nonlinear transformation function of the encoding process, which helps to map the encoded data in the latent feature space back to the original data space.

[0063] The weights of the decoding model are the parameters learned by the data decoding model during the training process, which determine the importance of each feature in the encoded data in the decoding process.

[0064] The decoding bias and encoding bias are also learnable parameters that allow the model to fine-tune each neuron of the decoded output. The bias term can adjust the baseline level of the decoding results, thereby increasing the flexibility and accuracy of the model.

[0065] The data decoding model maps the encoded data to the original data space and generates the corresponding decoding results. It is a key step in the sewage treatment data processing mechanism, which can reconstruct the input data from the potential feature space, thereby providing support for tasks such as data analysis, anomaly detection or prediction.

[0066] Based on the above data encoding processing model, different time series data sets are received and corresponding encoding information is generated accordingly. Subsequently, the relevant encoding information will be sent to the data decoding model to obtain the decoding result. In order to effectively evaluate the accuracy of the decoding results of different time series data and further understand the performance differences of the model on different time series, a data error prediction model is constructed in this embodiment, and the error coefficients corresponding to different time series information are analyzed and predicted based on the above model.

[0067] The above data error analysis model satisfies the following relationship:

[0068] in, represents the error coefficient of different time series data sets, express The amount of data, The number of time series representing sewage treatment information, express The feature information matrix, express The corresponding coefficient of variation is express A collection of time series data, Represents the decoding results of different time series data sets.

[0069] The error coefficient of different time series data sets is a quantitative indicator that can measure the degree of difference or error between the decoding result and the original data. The smaller the error coefficient, the higher the accuracy of the decoding result.

[0070] The number of data points contained in the time series data set can be normalized when calculating the error coefficient to ensure that time series of different lengths are comparable in error assessment.

[0071] The number of time series of sewage treatment information, that is, the total number of time series is i.

[0072] The feature information matrix of different time series data sets can be a two-dimensional array or matrix, which contains the feature information of the time series data set. The relevant features can be the statistics of the time series data, the transformed values ​​or other quantities that can describe the characteristics of the data.

[0073] The coefficient of variation corresponding to the feature information matrix is ​​a scalar value that can be used to measure the degree of change of the eigenvalues ​​in the feature information matrix. The above coefficient of variation reflects the volatility, complexity or other properties related to feature changes of the time series data.

[0074] The above-mentioned data error analysis model can receive the decoding results and the original time series data set as input. Through the calculation and analysis process, it will output the error coefficient corresponding to different time series data. This coefficient quantifies the difference or error between the original data and the decoding results, thereby providing technical support for the optimal processing of rural sewage treatment data sets.

[0075] Then, the rural sewage treatment data set is processed using the sewage treatment data processing mechanism to obtain the target prediction data set of rural sewage treatment stations.

[0076] The rural sewage treatment data set is processed through the data encoding model to obtain the encoding results of different time series data sets; the encoding results of different time series data sets are decoded using the data decoding model to obtain the decoding results of different time series data sets; the data sets and decoding results of different time series are subjected to error analysis based on the data error analysis model to obtain the anomaly judgment results of different time series data subsets; different time series data subsets are optimized based on the anomaly judgment results, so as to obtain the target prediction data set of the rural sewage treatment station.

[0077] In an optional embodiment, first, the rural sewage treatment data set is processed using a data coding model, based on which the original data is converted into a coding form that is more suitable for subsequent analysis and processing, thereby obtaining coding results of different time series data sets.

[0078] Then, the data decoding model is used to decode the encoding results of different time series data sets. The above decoding process is a process of converting the encoded data back to the original data space or an approximate original data space, thereby obtaining the decoding results of different time series data sets.

[0079] In order to evaluate the accuracy of the decoding results, error analysis and anomaly judgment are carried out. The decoding results are compared with the original time series data set based on the data error analysis model. If the error is large or exceeds the preset threshold, it can be determined that there is an anomaly in the time series data subset. The above threshold can be set and adjusted based on the historical information of the rural sewage treatment station.

[0080] Finally, data optimization and target prediction data set acquisition are performed. Based on the abnormal judgment results of the time series data set, different time series data sets are optimized to eliminate or reduce the impact of abnormal data. The optimized data sets will be used as the target prediction data set for rural sewage treatment stations.

[0081] For details on the sewage treatment data processing mechanism, please refer to Figure 2 ,in represents the rural sewage treatment data set, Represents the encoding results of different time series data sets, Represents the decoding results of different time series data sets, represents the error coefficient of different time series data sets, Represents the target monitoring data of rural sewage treatment.

[0082] Furthermore, the target prediction data set of the rural sewage treatment station in the embodiment satisfies the following relationship:

[0083] in, represents the target monitoring data of rural sewage treatment, Indicates the optimized Data subset, Indicates the optimized Data subset, Indicates the optimized Data subset.

[0084] In summary, through the data encoding and decoding process in the sewage treatment data processing mechanism, the key information in the sewage treatment data can be extracted and processed more effectively; the error analysis and anomaly determination steps can identify and exclude outliers in the data, thereby improving the overall accuracy of the sewage treatment data, and then obtaining accurate and reliable rural sewage treatment target prediction data sets and monitoring data. The above processing mechanism provides a clear and intuitive method for abnormal data detection and optimization.

[0085] Furthermore, the method for obtaining the rural sewage treatment target prediction data set in this embodiment is only an optional condition of the present invention. In one or some other embodiments, the method for obtaining the sewage treatment data set can be optimized according to the actual situation of the rural sewage treatment data and the needs of sewage treatment prediction. Different rural areas face different sewage treatment challenges, such as water quality differences, changes in treatment scale, etc. By optimizing the data set acquisition method, it is possible to better adapt to different conditions and ensure the scientificity and practicality of the target prediction data set.

[0086] S3. Construct a rural sewage treatment result prediction and analysis system. Combine the above target prediction data set and the rural sewage treatment result prediction and analysis system to obtain the evaluation prediction results of the prediction and analysis system. The specific implementation contents are as follows: First, the reference index layer of the rural sewage treatment result prediction and analysis system is set based on the historical information of rural sewage treatment. In the embodiment, the above reference index layer mainly includes a water quality reference index layer, a treatment efficiency reference index layer and a sewage process performance reference index layer.

[0087] In order to accurately predict and evaluate the treatment results of rural sewage in the rural sewage treatment result prediction method, it is first necessary to set up a prediction and analysis system based on historical information and prediction needs. Selecting an appropriate reference indicator layer is conducive to constructing a rural sewage treatment result prediction and analysis system, which is directly related to the accuracy and reliability of the prediction results.

[0088] The reference index layer of the rural sewage treatment result prediction and analysis system in the embodiment mainly includes three levels: a water quality reference index layer, a treatment efficiency reference index layer and a sewage process performance reference index layer.

[0089] The water quality reference index layer mainly focuses on various water quality indicators of sewage, such as chemical oxygen demand (COD), biochemical oxygen demand (BOD), suspended solids (SS), ammonia nitrogen (NH3-N), etc. The above indicators can directly reflect the pollution degree of sewage and the improvement of water quality after treatment, and are the key basis for evaluating the effect of sewage treatment.

[0090] The treatment efficiency reference index layer is an important indicator for measuring the performance of the sewage treatment system. It is necessary to pay attention to the removal rate, treatment speed and energy consumption in the sewage treatment process to comprehensively evaluate the effectiveness of the sewage treatment system. The above indicators are helpful to understand the operating status of the sewage treatment system, discover potential problems in time and make optimization adjustments.

[0091] The sewage process performance reference index layer is a key indicator reflecting the stability and applicability of the sewage treatment process. Further attention should be paid to the stability, flexibility and adaptability of the sewage process to different water quality conditions. The above indicators are helpful to evaluate the advantages and disadvantages of existing processes and provide guidance information for the optimization and improvement of sewage treatment strategies.

[0092] When selecting and extracting reference indicators at different reference indicator levels, it is necessary to refer to sewage treatment expertise and experience, as well as rural sewage treatment data sets. At the same time, statistical methods or machine learning algorithms are used to screen and optimize reference indicators to ensure that the selected indicators can truly and accurately reflect the sewage treatment situation. The above process not only improves the performance of the prediction and evaluation system, but also lays the foundation for subsequent data processing and analysis.

[0093] For information on the rural sewage treatment result prediction and analysis system of this embodiment, please refer to Table 1.

[0094] Table 1 Rural sewage treatment result prediction and analysis system table

[0095] In summary, scientifically and reasonably setting the reference indicator layer to construct a rural sewage treatment result prediction and analysis system can more accurately predict and evaluate the sewage treatment effect and provide strong support for rural sewage treatment.

[0096] Then, based on the water quality reference index layer, the BOD analysis results, COD analysis results, suspended solids analysis results and total phosphorus analysis results can be obtained; according to the treatment efficiency reference index layer, the pollutant removal rate analysis results and sewage treatment volume analysis results can be obtained; according to the sewage process performance reference index layer, the sludge analysis results and sludge moisture content analysis results can be obtained.

[0097] In an optional embodiment, BOD analysis results, COD analysis results, suspended matter analysis results and total phosphorus analysis results are obtained based on the water quality reference indicator layer.

[0098] BOD (biochemical oxygen demand) in water quality reference index layer: BOD refers to the mass concentration of dissolved oxygen required by microorganisms in water to decompose organic matter in an aerobic environment. The above process mainly reflects the biochemical transformation. The above BOD is an important scale for measuring the content of biodegradable organic matter in sewage, and is of great significance for evaluating the efficiency and effect of sewage treatment systems.

[0099] In this embodiment, the time for microorganisms to decompose organic matter in water is set to 15 days, and the BOD analysis results need to satisfy the following relationship:

[0100] in, Represents the biochemical oxygen demand in the sewage sample for 15 days, represents the initial dissolved oxygen of the sewage sample after dilution, It indicates the dissolved oxygen of the diluted sewage sample after being cultured in a constant temperature incubator for 15 days. Indicates the dilution ratio of the sewage sample; The 15-day biochemical oxygen demand in sewage samples, that is, the mass concentration of dissolved oxygen consumed by microorganisms in water to decompose organic matter under aerobic conditions within 15 days, is an important indicator to measure the content of biodegradable organic matter in sewage.

[0101] The initial dissolved oxygen of the diluted sewage sample, that is, the dissolved oxygen concentration in the sewage sample before the BOD analysis experiment begins, is also the starting point for BOD measurement.

[0102] The dissolved oxygen concentration of the diluted sewage sample after 15 days of incubation in a constant temperature incubator, that is, the dissolved oxygen concentration in the water sample after the BOD analysis experiment is completed, is compared and The difference in dissolved oxygen consumed by the microorganisms in 15 days can be used to calculate the amount of dissolved oxygen consumed by the microorganisms in 15 days.

[0103] The dilution ratio of sewage samples refers to the degree to which the original sewage samples are diluted. The selection of the dilution ratio is crucial for the measurement of BOD, based on which it can ensure that the organic matter in the water sample is fully oxidized and decomposed during the measurement process, and the above sewage sample dilution ratio needs to satisfy the following relationship:

[0104] in, represents the dilution ratio of the sewage sample, represents the initial volume of the sewage sample, Indicates the final volume of the sewage sample after dilution.

[0105] At the same time, different situations The calculation formula is as follows:

[0106] in, express load, represents the sewage sample flow rate, Indicates the BOD concentration of the sewage sample.

[0107] COD (Chemical Oxygen Demand) in the water quality reference index layer: Example Based on the analysis results of COD (chemical oxygen demand) determined by potassium permanganate (KMnO4) method, the COD value reflects the amount of chemical oxygen demand in the sewage sample, can comprehensively reflect the amount of all organic matter in the water, and is an important indicator for water quality monitoring.

[0108] The above COD analysis results satisfy the following relationship:

[0109] in, Represents the chemical oxygen demand in the sewage sample, The conversion factor representing the number of moles of oxygen required to oxidize 1 mol of organic matter and the number of moles of KMnO4, represents the molar mass of oxygen atoms, Indicates the difference in KMnO4 dosage before and after the sewage sample titration. Indicates the standard concentration of KMnO4, It indicates the volume of sewage sample after adding distilled water; The COD analysis formula takes into account factors such as the dosage, concentration and sample volume of potassium permanganate, and can accurately calculate the chemical oxygen demand in sewage samples. At the same time, it also reflects the importance of COD as a water quality monitoring indicator, which can comprehensively reflect the amount of all organic matter in the water.

[0110] The amount of oxygen consumed by a chemical oxidant (in this case potassium permanganate KMnO4) to oxidize all oxidizable substances.

[0111] The conversion coefficient represents the proportional relationship between the number of moles of oxygen required to oxidize 1 mol of organic matter and the number of moles of potassium permanganate KMnO4 consumed. Since different organic matter is oxidized by potassium permanganate at different efficiencies, the corresponding coefficient is required to convert it, which can more accurately reflect the amount of oxygen consumed by the organic matter.

[0112] The molar mass of oxygen atoms is the mass of each mole of oxygen atoms. Converting the number of moles of consumed oxygen into mass is helpful in obtaining the actual value of chemical oxygen demand.

[0113] The change in the amount of potassium permanganate KMnO4 used during the titration of sewage samples is usually expressed in moles or milliliters, which further reflects the degree of oxidation of organic matter in the sewage samples and is one of the key parameters for calculating COD.

[0114] The concentration of potassium permanganate solution can be used to calculate the number of moles of potassium permanganate consumed during the titration.

[0115] The total volume of the sewage sample after adding distilled water can convert the molar number of potassium permanganate consumed into the chemical oxygen demand per unit volume, thereby obtaining the COD concentration value.

[0116] The above formula and its parameters together constitute the analytical function for determining COD based on the potassium permanganate method. By accurately measuring and calculating the relevant parameters, the chemical oxygen demand in the sewage sample can be obtained, and then the degree of organic pollution in the water body can be evaluated.

[0117] Suspended matter in the water quality reference index layer: Suspended solids refer to solid particulate matter that cannot pass through the 2mm sieve and is further retained on the 1μm pore filter paper. In the field of sewage treatment technology, the above substances are objects that need to be removed. The level of suspended solids is directly related to the clarity of the water body and the overall water quality. When the suspended solids content is too high, the transparency of the water body will decrease, which will affect the beauty and use function of the water body. At the same time, suspended solids may also carry a large amount of harmful substances, posing a threat to aquatic life and human health. Therefore, in the sewage treatment process, the effective removal and accurate measurement of suspended solids are crucial.

[0118] The calculation formula of suspended solids can be used to quantify the content of suspended solids in rural sewage water bodies. The above suspended solids analysis results satisfy the following relationship:

[0119] in, represents the calculation result of suspended solids in sewage samples, Indicates the mass of suspended matter in a sewage sample. Indicates the sewage sample flow rate; The calculation result of suspended solids in sewage samples refers to the suspended solids concentration in sewage samples, which reflects the content of suspended solids in sewage samples and is one of the important indicators for evaluating the degree of rural water pollution.

[0120] In the embodiment, the filtration method is used to measure the mass of suspended matter in the sewage sample. A certain volume of sewage sample is filtered through filter paper (filter paper with a pore size of 1 μm), and then the solid matter on the filter paper is dried and weighed to obtain the mass. .

[0121] The flow rate of sewage samples is measured by flow meters and other equipment, and the selection of flow meters should be adjusted and determined according to the size of sewage flow, measurement accuracy and experimental requirements.

[0122] Total phosphorus in water quality reference index layer: The calculation formula of total phosphorus is used to quantify the total phosphorus content in water samples in sewage treatment and water quality monitoring. The above total phosphorus analysis results satisfy the following relationship:

[0123] in, represents the total phosphorus concentration in the sewage sample, Represents the measured value of total phosphorus in sewage samples, Indicates the dilution factor of the sewage sample. Indicates the impact index for measuring total phosphorus content; The total phosphorus concentration in sewage samples indicates the mass of total phosphorus per unit volume of sewage.

[0124] The measurement value of total phosphorus in the sewage sample can be obtained by the absorbance value obtained by chemical analysis methods such as spectrophotometry, and the above absorbance value is proportional to the total phosphorus content in the sewage sample.

[0125] The dilution factor of the sewage sample is the factor by which the original sewage sample is diluted before measurement. Since the total phosphorus content in the sewage sample is high, direct measurement will exceed the measurement range of the instrument or affect the accuracy of the measurement, so it is necessary to reduce the concentration by dilution so that it falls within the normal measurement range of the instrument.

[0126] The total phosphorus content influence index reflects the degree of influence of various factors on the results during the measurement process. The above parameters are a comprehensive factor, including but not limited to instrument error, reagent purity, operating conditions and other factors. In practical applications, the value of the influence index needs to be defined and calibrated according to experimental conditions and analytical methods.

[0127] In this embodiment, the BOD analysis result, COD analysis result, suspended matter analysis result and total phosphorus analysis result are obtained based on the analysis functions of different reference indicators in the water quality reference indicator layer.

[0128] BOD analysis results can evaluate the biodegradability of wastewater. As a measure of the content of biodegradable organic matter in sewage, it helps to understand the biological treatment potential of sewage. By guiding sewage treatment technology, BOD analysis can effectively analyze the operating parameters of the sewage treatment system, which helps to improve the treatment efficiency of rural sewage treatment result prediction methods. In terms of environmental monitoring and protection, BOD data can be used to evaluate the self-purification capacity of water bodies and the health of ecosystems.

[0129] The COD analysis results are conducive to the rapid assessment of organic pollution. As an important indicator for water quality monitoring, COD can quickly reflect the total amount of organic matter in the water body. It is conducive to process control and optimization. In the sewage treatment process, COD data can be used to adjust the dosage of chemical oxidants to further ensure the treatment effect of the rural sewage treatment result prediction method. It is helpful for pollution source tracking and control. By analyzing COD data in different regions, it helps to identify the source of organic pollution.

[0130] The results of suspended matter analysis can improve water transparency. Reducing the suspended matter content helps improve the transparency and aesthetics of the water. It is beneficial to protect aquatic life. Reducing suspended matter can alleviate the negative impact on aquatic life and maintain ecological balance. It can also optimize filtration technology. By analyzing suspended matter data, the filtration process in sewage treatment can be optimized to improve the final effluent quality.

[0131] The results of total phosphorus analysis can prevent eutrophication of water bodies. Total phosphorus is one of the key factors that cause eutrophication of water bodies. Controlling the total phosphorus content helps prevent water quality deterioration. It helps guide the use of phosphate fertilizers. In agriculture and horticulture, total phosphorus data can be used to guide the rational use of phosphate fertilizers and reduce phosphorus loss. It is beneficial to environmental policy making. Total phosphorus data provides a scientific basis for the formulation and implementation of environmental protection policies.

[0132] In summary, the analysis results of BOD, COD, suspended solids and total phosphorus in the water quality reference index layer not only help to understand the pollution status of the water body, but also provide data support for the optimization of sewage treatment technology, the formulation of environmental policies and ecological protection plans, and jointly promote the sustainable use of water resources and the realization of environmental protection goals.

[0133] In an optional embodiment, the pollutant removal rate analysis results and the sewage treatment volume analysis results are obtained according to the treatment efficiency reference index layer.

[0134] Analysis results of pollutant removal rate in the treatment efficiency reference index layer: The difference in pollutant concentration before and after sewage treatment and the ratio of sewage sample concentrations and other related information are used to further analyze the proportion or efficiency of pollutants removed from sewage samples during sewage treatment. The above pollutant removal rate analysis results meet the following relationship:

[0135] in, It represents the removal rate of pollutant concentration in sewage samples, represents the initial pollutant concentration of the sewage sample, represents the initial volume of the sewage sample, represents the pollutant concentration of sewage after treatment on day n, Represents the volume of the sewage sample after treatment on day n The removal rate of pollutant concentration in sewage samples indicates the proportion or efficiency of pollutants removed during sewage treatment, which in turn reflects the purification capacity of sewage treatment processes for specific pollutants.

[0136] The initial pollutant concentration of a sewage sample refers to the concentration of pollutants in the sewage sample before treatment, and further provides a benchmark for the degree of sewage contamination before treatment.

[0137] The initial volume of the sewage sample is the volume of the sewage sample before treatment, reflecting the volume of the sewage sample before treatment.

[0138] The sewage pollutant concentration after the nth day of treatment refers to the concentration of pollutants in the sewage sample after n days of treatment, which indicates the residual amount of pollutants in the sewage sample after treatment.

[0139] The volume of the sewage sample on the nth day after treatment refers to the volume of the sewage sample after n days of treatment. Since water evaporation, sludge generation, etc. will occur during the treatment process, the volume of the sewage sample will change, so it is necessary to further consider the volume change of the sewage sample.

[0140] Analysis results of sewage treatment volume in the treatment efficiency reference index layer: Based on the rural sewage treatment data set of rural sewage treatment stations, the daily / monthly / annual treatment volume data of the sewage treatment stations are obtained. In addition to directly analyzing the treatment volume at a certain point in time, the daily, monthly or annual treatment volume can also be calculated, thereby reflecting the treatment capacity of sewage treatment facilities on different time scales.

[0141] It is also possible to analyze the trend of changes in the treatment volume of rural sewage treatment stations. By drawing a curve chart showing the change in treatment volume over time, the trend of changes in treatment volume can be intuitively displayed, including increase, decrease or stability. By comparing the actual treatment volume with the preset treatment capacity, it is possible to evaluate whether the operating efficiency of the sewage treatment facility meets the design requirements, and whether there is overload operation or idle treatment capacity, so as to have a more comprehensive understanding of the analysis results of the sewage treatment volume, as well as the performance and improvement space of sewage treatment facilities in different aspects, which will help rural sewage treatment stations to formulate more reasonable sewage treatment strategies and optimization plans.

[0142] In an optional embodiment, the sludge analysis results and the sludge moisture content analysis results are obtained based on the sewage process performance reference indicator layer.

[0143] Sludge analysis results in the sewage process performance reference index layer: Sludge output refers to the total amount of solid waste produced due to physical, chemical or biological effects during the sewage treatment process. The above solid waste mainly includes sludge composed of suspended matter, colloids, organic matter, inorganic matter and microorganisms.

[0144] Sludge production not only directly reflects the operating efficiency of the sewage treatment process, such as the removal of suspended solids during the treatment process, but is also an important basis for evaluating the cost of sludge treatment, including the collection, transportation, treatment and disposal of sludge. At the same time, the amount of sludge production also indirectly affects the decision-making of the operation management, equipment maintenance and environmental protection of the sewage treatment plant.

[0145] The above sludge analysis results satisfy the following relationship:

[0146] in, represents the sedimentation volume of sludge in the sewage sample, It represents the sedimentation efficiency of the sedimentation tank. represents the sewage sample flow rate, Indicates the suspended solids concentration of the sewage sample entering the sedimentation tank. Indicates the moisture content of sludge in sewage samples, represents the initial sludge concentration in the sedimentation tank, It represents the influencing parameter corresponding to the number of sewage treatment inlets in the sewage treatment system, Indicates the influencing parameter of the interval time of sludge discharge from the sedimentation tank in sewage treatment; The sedimentation volume of sludge in sewage samples indicates the volume occupied by sludge after a certain period of sedimentation in the sedimentation tank. It is an important indicator for evaluating sludge production and sedimentation tank efficiency.

[0147] The sedimentation efficiency of a sedimentation tank is a dimensionless parameter that can be used to describe the ability of the sedimentation tank to remove suspended matter. The higher the sedimentation efficiency during sewage treatment, the better the effect of the sedimentation tank in removing suspended matter.

[0148] The sewage sample flow rate indicates the amount of sewage flowing into the sedimentation tank per unit time. The larger the flow rate, the more sewage the sedimentation tank treats, and the corresponding amount of sludge produced also increases.

[0149] The suspended solids concentration of the sewage sample entering the sedimentation tank indicates the ratio of the mass or volume of the suspended solids in the sewage to the total volume of the sewage. The higher the suspended solids concentration, the more solid particles the sewage contains, and the correspondingly greater amount of sludge produced.

[0150] The moisture content of sludge in sewage samples is a percentage value, which indicates the proportion of water in the sludge. The higher the moisture content, the more water there is in the sludge, and vice versa, the less dry matter content there is in the sludge.

[0151] The initial sludge concentration of the sedimentation tank refers to the ratio of the mass or volume of dry matter in the sludge to the total volume of the sludge before the sedimentation tank starts working. The initial sludge concentration will affect the settling rate of the sludge during the sedimentation process and the processing capacity of the sedimentation tank.

[0152] The influencing parameters corresponding to the number of sewage treatment inlets in the sewage treatment system refer to the parameters related to the design and operation of the sewage treatment system, which can be used to describe the impact of the number of sewage treatment inlets on sludge production, which involves factors such as sewage diversion, merging, and treatment process.

[0153] The influencing parameters of the sludge discharge interval of the sedimentation tank in sewage treatment refer to the parameters related to the operation of the sedimentation tank, and further describe the impact of the sludge discharge interval on the sludge production. The longer the sludge discharge interval, the more sludge accumulates in the sedimentation tank, but too long an interval will cause sludge concentration and compaction, affecting the sedimentation effect.

[0154] Based on the above parameters, the generation and treatment of sludge in the sewage treatment process are comprehensively evaluated. At the same time, in practical applications, the formulas and parameters need to be adjusted and optimized according to factors such as sewage treatment process, equipment, and water quality.

[0155] Analysis results of sludge moisture content in the sewage process performance reference index layer: The moisture content of sludge refers to the proportion of water in the sludge. Based on this, it can reflect the humidity or dryness of the sludge, that is, it can further analyze the mass proportion of the remaining water after removing the solid matter in the total mass of the sludge, that is, the mass after drying.

[0156] The above-mentioned sludge moisture content not only affects the volume, weight and transportation cost of the sludge, but also affects the subsequent treatment and disposal of the sludge. Sludge with high moisture content is large in volume and heavy in weight, which increases the difficulty of transportation and treatment; at the same time, high moisture content is not conducive to the stabilization and resource utilization of sludge. Therefore, reducing the moisture content of sludge is a key link in the sludge treatment and disposal process.

[0157] In an optional embodiment, a certain amount of sewage sample is taken and the total mass of the sludge therein is measured using a weighing device. The sludge is placed in an oven set at a temperature of 105°C for drying until the sludge sample reaches a constant weight. The dried sludge sample is taken out and its mass is measured again using a weighing device, i.e., the dry mass. , and then obtain the analysis results of the sludge moisture content in the sewage sample.

[0158] The sludge moisture content analysis results in the embodiment satisfy the following relationship:

[0159] in, Indicates the moisture content of sludge in sewage samples, represents the total mass of sludge in the sewage sample, Indicates the mass of solid matter in sludge after removing water The moisture content of sludge in sewage samples indicates the proportion of water contained in the sludge in the sewage samples, and is used to quantify the humidity or dryness of the sludge. The higher the moisture content, the more water content in the sludge; conversely, the lower the moisture content, the drier the sludge.

[0160] The total mass of sludge in a sewage sample refers to the mass of the sludge before any treatment or drying, which mainly includes all the components in the sludge, namely water and solid matter. The above parameters are the basis for calculating the moisture content and can be used to determine the initial mass of the sludge.

[0161] The mass of solid matter after removing water from sludge is the mass obtained after removing water from sludge through drying methods such as oven drying, vacuum drying, etc. The above parameters directly reflect the mass of water-insoluble solid components in sludge and are key data for calculating moisture content.

[0162] This embodiment designs a multi-dimensional reference indicator layer in the analysis and prediction of the rural sewage treatment results of the analysis system, which mainly covers the three reference indicator layers of water quality, treatment efficiency and sewage process performance, and can thus evaluate the effect and performance of rural sewage treatment from all aspects and angles.

[0163] First, the water quality reference index layer can clearly understand the types and concentrations of pollutants in sewage and thus judge the water quality conditions by providing analysis results such as BOD, COD, suspended solids and total phosphorus. The above water quality indicators are the basis for evaluating the effectiveness of sewage treatment and an important basis for formulating subsequent treatment strategies.

[0164] Secondly, the treatment efficiency reference index layer intuitively demonstrates the treatment capacity and efficiency of sewage treatment facilities by calculating analysis results such as pollutant removal rate and sewage treatment volume. Relevant indicators help to timely discover bottlenecks and problems in the treatment process, and provide strong support for optimizing treatment processes and improving treatment efficiency.

[0165] Finally, the sewage process performance reference index layer deeply analyzes the performance and stability of the sewage treatment process through the results of sludge analysis and sludge moisture content analysis. The above indicators not only reflect the generation and treatment of sludge, but also provide important reference for the operation status and adjustment of the sewage treatment system.

[0166] In summary, selecting reference indicators that are closely related to sewage treatment results and building a complete prediction and analysis system can comprehensively and accurately reflect the effects and performance of rural sewage treatment. It not only helps to promptly discover and deal with problems and improve sewage treatment efficiency and quality, but also provides strong technical support and decision-making basis for rural sewage treatment, which is conducive to promoting rural environmental protection and sustainable development.

[0167] Furthermore, the analysis method for different reference indicators of the analysis system in the present embodiment is only an optional condition of the present invention. In one or some other embodiments, the analysis method for different reference indicators can be adjusted according to the prediction requirements of the rural sewage treatment results and the basic situation of the sewage treatment results. The situation of rural sewage treatment is complex and changeable. The sewage composition, concentration and treatment requirements in different regions and time periods are different. Adjusting the analysis method for different reference indicators according to the actual situation can ensure that the analysis system can flexibly adapt to various situations and improve the accuracy and practicality of the analysis results.

[0168] S4. Establish a sewage treatment index correlation analysis model, and conduct a comprehensive evaluation and analysis of rural sewage treatment results by combining the above sewage treatment index correlation analysis model, evaluation prediction results and target prediction data set. The specific implementation contents are as follows: Firstly, a sewage treatment index correlation analysis model was established based on the rural sewage treatment standards; and the sewage treatment index correlation analysis model was used to obtain the correlation coefficients of different reference indicators in the rural sewage treatment result prediction and analysis system.

[0169] The embodiment introduces rural sewage treatment standards to construct a sewage treatment index correlation analysis model, and uses the correlation analysis model to explore the intrinsic connection and influence coefficient between different sewage treatment indicators, as well as the correlation between different reference indicators and rural sewage treatment results.

[0170] In the model, the Pearson correlation coefficient is used as a statistical tool to quantify the strength of the correlation between different water quality reference indicators and rural sewage treatment standards. The correlation coefficient can intuitively reveal the degree and direction of the linear relationship between the indicators, and provide a numerical reference for positive or negative correlation.

[0171] The above sewage treatment index correlation analysis model satisfies the following relationship:

[0172] in, Represents the correlation coefficient of different reference indicators in the rural sewage treatment result prediction and analysis system, Represents the measured average value of different reference indicators, express The corresponding weight coefficient is, Indicates the reference values ​​of different reference indicators.

[0173] The correlation coefficient between different reference indicators is a quantitative indicator that can measure the strength and direction of the linear relationship between two or more indicators. It helps to intuitively understand the relationship between indicators and identify the influence and role of reference indicators in the sewage treatment process.

[0174] The measurement average reflects the average state or level of an indicator in multiple measurements. As a key input in the model, the accuracy of the measurement average directly affects the calculation results of the correlation coefficient.

[0175] The weight coefficients corresponding to the reference values ​​of different reference indicators, that is, in the correlation analysis, different indicators have different degrees of influence on the sewage treatment results and have different weight coefficients. The relevant weight coefficients enable the model to more flexibly reflect the actual situation. The indicators can be weighted in the model to more accurately evaluate the correlation between different reference indicators.

[0176] The reference values ​​of different reference indicators are mainly set based on historical data, industry standards or expert opinions, and can be used as a benchmark for evaluating current measurements. The reference value provides a comparison point for the model and can quantify the difference between the measured average and the reference value.

[0177] The correlation coefficient can be used to quantify the degree and direction of the linear relationship between indicators, thereby more accurately evaluating the correlation between different reference indicators and reducing the impact of subjectivity. Compared with expert opinions or empirical judgment methods, the sewage treatment indicator correlation analysis model can reduce subjectivity and uncertainty in the analysis process, provide support for subsequent sewage treatment decisions, and make sewage treatment plans more scientific and reasonable, which is important for promoting the scientific, standardized and efficient rural sewage treatment.

[0178] Then, a comprehensive evaluation and analysis of rural sewage treatment results was conducted by combining the correlation coefficient, evaluation prediction results and target prediction data set.

[0179] In this embodiment, the correlation coefficients of different reference indicators are analyzed based on the above-mentioned sewage treatment indicator correlation analysis model. The above-mentioned coefficients jointly reflect the degree and direction of the linear relationship between different indicators, and thus provide an intuitive understanding of the relationship between different indicators. The correlation analysis results are summarized in Table 2. The correlation information of each reference indicator in the prediction analysis system listed in Table 2 analyzes the correlation coefficients of each indicator in the water quality reference indicator layer (BOD, COD, suspended solids, total phosphorus), the treatment efficiency reference indicator layer (pollutant removal rate, sewage treatment volume) and the sewage process performance reference indicator layer (sewage sludge, sludge moisture content).

[0180] For information on the correlation coefficients of different reference indicators in this embodiment, please refer to Table 2. In the comprehensive evaluation and analysis process, the correlation coefficients of different reference indicators reflect the degree and direction of the linear relationship between the indicators and provide an intuitive understanding of the relationship between the indicators.

[0181] Table 2 Correlation information table of each reference indicator in the prediction analysis system

[0182] Based on the above implementation steps and relevant mathematical models, the rural sewage treatment result prediction and evaluation analysis process is as follows: Firstly, rural sewage treatment data were systematically collected through rural sewage treatment stations. The relevant data constituted the analysis basis of the treatment results. In order to effectively process the rural sewage treatment data set, a sewage treatment data processing mechanism was established. The rural sewage treatment data set was processed using the above mechanism. Through a series of data cleaning, conversion, analysis and iterative optimization steps, the target prediction data set of rural sewage treatment stations was finally obtained.

[0183] Next, a rural sewage treatment result prediction and analysis system was constructed. The system was combined with a target prediction data set. The prediction algorithm and model of the embodiment were used to obtain the evaluation prediction results of the prediction and analysis system. The results provided preliminary judgment information on the rural sewage treatment effect.

[0184] In order to gain a deeper understanding of the results of rural sewage treatment, a sewage treatment index correlation analysis model was also established. The above correlation analysis model combines the intrinsic relationship between sewage treatment indicators and the correlation between different reference indicators and rural sewage treatment results. The above model is combined with the evaluation prediction results and the target prediction data set to conduct a comprehensive comprehensive evaluation and analysis of rural sewage treatment results.

[0185] Through a series of steps and analyses, we can not only obtain the prediction results of rural sewage treatment, but also deeply understand the interaction and influence between different indicators, which provides strong support for the optimization and improvement of rural sewage treatment. It not only improves the accuracy and comprehensiveness of the analysis results, but also provides a reference basis for sewage treatment work.

[0186] See also Figure 3 In an optional embodiment, in order to efficiently execute a rural sewage treatment result prediction method provided by the present invention, the present invention also provides a rural sewage treatment result prediction system, the system includes a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and execute the rural sewage treatment result prediction method and related embodiments provided by the present invention. The rural sewage treatment result prediction system of the present invention has a complete structure, objective and stable.

[0187] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for predicting rural sewage treatment results, characterized in that: The method comprises: Collect rural sewage treatment data sets through rural sewage treatment stations; Establishing a sewage treatment data processing mechanism, using the sewage treatment data processing mechanism to process the rural sewage treatment data set, and obtaining a target prediction data set for rural sewage treatment stations; Constructing a rural sewage treatment result prediction and analysis system, and combining the target prediction data set with the rural sewage treatment result prediction and analysis system to obtain an evaluation prediction result of the prediction and analysis system; A sewage treatment index correlation analysis model is established, and a comprehensive evaluation and analysis of rural sewage treatment results is conducted by combining the sewage treatment index correlation analysis model, the evaluation prediction results and the target prediction data set.

2. The rural sewage treatment result prediction method according to claim 1, characterized in that: The rural sewage treatment data set collected through the rural sewage treatment station includes: Obtain historical information on rural sewage treatment based on rural sewage treatment stations; Collect real-time detection data of rural sewage treatment through rural sewage treatment stations; The rural sewage treatment data set is obtained by combining the rural sewage treatment historical information and the rural sewage treatment real-time detection data.

3. The rural sewage treatment result prediction method according to claim 1, characterized in that: The establishment of a sewage treatment data processing mechanism includes: Set up data encoding model, data decoding model and data error analysis model based on data time series characteristics; A sewage treatment data processing mechanism is established by combining the data encoding model, the data decoding model and the data error analysis model.

4. The rural sewage treatment result prediction method according to claim 3 is characterized in that: The data encoding model satisfies the following relationship: ， in, Represents the encoding results of different time series data sets, Represents the nonlinear change function of the encoding process, represents the encoding model weight, express A collection of time series data, Indicates encoding bias; The data decoding model satisfies the following relationship: ， in, Represents the decoding results of different time series data sets, Represents the constant corresponding to the decoding model, Represents the nonlinear variation function of the decoding process, represents the decoding model weight, Represents the encoding results of different time series data sets, represents the decoding bias; The data error analysis model satisfies the following relationship: ， in, represents the error coefficient of different time series data sets, express The amount of data, The number of time series representing sewage treatment information, express The feature information matrix, express The corresponding coefficient of variation is express A collection of time series data, Represents the decoding results of different time series data sets.

5. The rural sewage treatment result prediction method according to claim 3, characterized in that: The method of processing the rural sewage treatment data set by using the sewage treatment data processing mechanism and obtaining a target prediction data set for rural sewage treatment stations includes: Processing the rural sewage treatment data set by the data coding model to obtain coding results of different time series data sets; Using the data decoding model to decode the encoding results of different time series data sets, to obtain decoding results of different time series data sets; According to the data error analysis model, error analysis is performed on the data sets of different time series and the decoding results to obtain abnormality determination results of the data sets of different time series; Different time series data sets are optimized based on the abnormality determination results to obtain a target prediction data set for a rural sewage treatment station.

6. The rural sewage treatment result prediction method according to claim 2, characterized in that: The construction of the rural sewage treatment result prediction and analysis system includes: Based on the rural sewage treatment historical information, a reference index layer of the rural sewage treatment result prediction and analysis system is set, and the reference index layer includes a water quality reference index layer, a treatment efficiency reference index layer and a sewage process performance reference index layer.

7. The rural sewage treatment result prediction method according to claim 6, characterized in that: The evaluation prediction results of the prediction analysis system obtained by combining the target prediction data set and the rural sewage treatment result prediction analysis system include: Obtaining BOD analysis results, COD analysis results, suspended matter analysis results and total phosphorus analysis results based on the water quality reference index layer; Obtaining pollutant removal rate analysis results and sewage treatment volume analysis results according to the treatment efficiency reference index layer; The sludge analysis results and the sludge moisture content analysis results are obtained based on the sewage process performance reference index layer.

8. The rural sewage treatment result prediction method according to claim 7, characterized in that: The BOD analysis results satisfy the following relationship: ， in, Represents the biochemical oxygen demand in the sewage sample for 15 days, represents the initial dissolved oxygen of the sewage sample after dilution, It indicates the dissolved oxygen of the diluted sewage sample after being cultured in a constant temperature incubator for 15 days. Indicates the dilution ratio of the sewage sample; The COD analysis results satisfy the following relationship: ， in, Represents the chemical oxygen demand in the sewage sample, The conversion factor representing the number of moles of oxygen required to oxidize 1 mol of organic matter and the number of moles of KMnO4, represents the molar mass of oxygen atoms, Indicates the difference in KMnO4 dosage before and after the sewage sample titration. represents the standard concentration of KMnO4, It indicates the volume of sewage sample after adding distilled water; The suspended matter analysis results satisfy the following relationship: ， in, represents the calculation result of suspended solids in sewage samples, Indicates the mass of suspended matter in a sewage sample, Indicates the sewage sample flow rate; The total phosphorus analysis results satisfy the following relationship: ， in, represents the total phosphorus concentration in the sewage sample, Represents the measured value of total phosphorus in sewage samples, Indicates the dilution factor of the sewage sample. Indicates the impact index for measuring total phosphorus content; The pollutant removal rate analysis results satisfy the following relationship: ， in, It represents the removal rate of pollutant concentration in sewage samples, represents the initial pollutant concentration of the sewage sample, represents the initial volume of the sewage sample, represents the pollutant concentration of sewage after treatment on day n, Represents the volume of the sewage sample after treatment on day n The sludge analysis results satisfy the following relationship: ， in, represents the sedimentation volume of sludge in the sewage sample, It represents the sedimentation efficiency of the sedimentation tank. represents the sewage sample flow rate, Indicates the suspended solids concentration of the sewage sample entering the sedimentation tank. Indicates the moisture content of sludge in sewage samples, represents the initial sludge concentration in the sedimentation tank, It represents the influencing parameter corresponding to the number of sewage treatment inlets in the sewage treatment system, Indicates the influencing parameter of the interval time of sludge discharge from the sedimentation tank in sewage treatment; The sludge moisture content analysis results satisfy the following relationship: ， in, Indicates the moisture content of sludge in sewage samples, represents the total mass of sludge in the sewage sample, It indicates the mass of solid matter in sludge after removing water.

9. The rural sewage treatment result prediction method according to claim 1, characterized in that: The establishment of a sewage treatment index correlation analysis model, and the comprehensive evaluation and analysis of rural sewage treatment results in combination with the sewage treatment index correlation analysis model, the evaluation prediction results and the target prediction data set include: Establish a sewage treatment index correlation analysis model based on rural sewage treatment standards; The sewage treatment index correlation analysis model is used to obtain the correlation coefficients of different reference indicators in the rural sewage treatment result prediction and analysis system; Conducting a comprehensive evaluation and analysis of rural sewage treatment results by combining the correlation coefficient, the evaluation prediction results and the target prediction data set; The sewage treatment index correlation analysis model satisfies the following relationship: ， in, Represents the correlation coefficient of different reference indicators in the rural sewage treatment result prediction and analysis system, Represents the measured average value of different reference indicators, express The corresponding weight coefficient is, Indicates the reference values ​​of different reference indicators.

10. A rural sewage treatment result prediction system, characterized in that: The system includes a processor, an input device, an output device and a memory, wherein the processor, input device, output device and memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the rural sewage treatment result prediction method as described in any one of claims 1-9.

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